#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
汽车行业与数学研究综合程序(智能增强版)
功能：
1. 汽车市场数据分析
2. 汽车技术参数计算
3. 数学建模应用
4. 汽车成本分析
5. 汽车供应链分析
6. 智能体技术支持
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt

class AutomotiveMarketAnalysis:
    """汽车市场数据分析模块"""
    
    def __init__(self):
        self.data = None
    
    def load_market_data(self, filepath):
        """加载市场数据"""
        self.data = pd.read_csv(filepath)
        return self.data.head()
    
    def sales_trend_analysis(self):
        """销售趋势分析"""
        if self.data is None:
            raise ValueError("请先加载市场数据")
        
        # 示例分析逻辑
        yearly_sales = self.data.groupby('Year')['Sales'].sum()
        yearly_sales.plot(kind='bar')
        plt.title("汽车年销售趋势")
        plt.xlabel("年份")
        plt.ylabel("销量")
        plt.show()
        
        return yearly_sales

class AutomotiveTechnicalCalculations:
    """汽车技术参数计算模块"""
    
    @staticmethod
    def calculate_power(engine_size, efficiency=0.85):
        """
        计算发动机功率
        参数:
            engine_size: 发动机排量(L)
            efficiency: 效率系数(默认0.85)
        返回:
            估算功率(马力)
        """
        return engine_size * 100 * efficiency
    
    @staticmethod
    def fuel_consumption(distance, fuel_used):
        """
        计算燃油消耗率
        参数:
            distance: 行驶距离(km)
            fuel_used: 燃油消耗量(L)
        返回:
            百公里油耗(L/100km)
        """
        return (fuel_used / distance) * 100

    @staticmethod
    def battery_range(battery_capacity, energy_consumption):
        """
        计算新能源车电池续航里程
        参数:
            battery_capacity: 电池容量(kWh)
            energy_consumption: 能耗(kWh/100km)
        返回:
            估算续航里程(km)
        """
        return (battery_capacity / energy_consumption) * 100

    @staticmethod
    def charging_time(battery_capacity, charger_power, current_soc=0.2):
        """
        计算新能源车充电时间
        参数:
            battery_capacity: 电池容量(kWh)
            charger_power: 充电功率(kW)
            current_soc: 当前电量状态(0-1, 默认0.2)
        返回:
            估算充电时间(小时)
        """
        return (battery_capacity * (1 - current_soc)) / charger_power

    @staticmethod
    def energy_consumption(distance, energy_used):
        """
        计算新能源车能耗
        参数:
            distance: 行驶距离(km)
            energy_used: 消耗能量(kWh)
        返回:
            百公里能耗(kWh/100km)
        """
        return (energy_used / distance) * 100

class MathematicalResearch:
    """数学研究应用模块"""
    
    @staticmethod
    def linear_regression(x, y):
        """
        线性回归分析
        参数:
            x: 自变量数组
            y: 因变量数组
        返回:
            斜率, 截距, 相关系数
        """
        slope, intercept = np.polyfit(x, y, 1)
        correlation = np.corrcoef(x, y)[0, 1]
        return slope, intercept, correlation
    
    @staticmethod
    def differential_equation_solver(func, initial_condition, t_range):
        """
        常微分方程数值解
        参数:
            func: 微分方程函数
            initial_condition: 初始条件
            t_range: 时间范围
        返回:
            数值解
        """
        from scipy.integrate import odeint
        solution = odeint(func, initial_condition, t_range)
        return solution

    @staticmethod
    def polynomial_regression(x, y, degree=2):
        """
        多项式回归分析
        参数:
            x: 自变量数组
            y: 因变量数组
            degree: 多项式次数(默认2)
        返回:
            多项式系数数组(从高次到低次)
        """
        coeffs = np.polyfit(x, y, degree)
        return coeffs.tolist()

    @staticmethod
    def time_series_analysis(data, window_size=3):
        """
        时间序列分析(移动平均)
        参数:
            data: 时间序列数据
            window_size: 移动窗口大小(默认3)
        返回:
            平滑后的时间序列
        """
        smoothed = []
        for i in range(len(data)):
            start = max(0, i - window_size + 1)
            end = i + 1
            smoothed.append(sum(data[start:end]) / (end - start))
        return smoothed

    @staticmethod
    def standardize_data(data):
        """
        数据标准化处理(Z-score标准化)
        参数:
            data: 原始数据数组
        返回:
            标准化后的数据
        """
        mean = np.mean(data)
        std_dev = np.std(data)
        return [(x - mean) / std_dev for x in data]

class AutomotiveCostAnalysis:
    """汽车成本分析类"""
    
    def __init__(self):
        """初始化成本分析工具"""
        pass

    def material_cost_analysis(self, components):
        """汽车材料成本分析
        参数:
            components: dict, 包含部件及对应成本 {'部件名': 成本}
        返回:
            total_cost: float, 总材料成本
            cost_distribution: dict, 成本分布
        """
        total_cost = sum(components.values())
        cost_distribution = {k: v/total_cost for k, v in components.items()}
        return total_cost, cost_distribution

    def production_cost_estimate(self, labor_hours, hourly_rate, material_cost):
        """生产成本估算
        参数:
            labor_hours: float, 总工时
            hourly_rate: float, 小时工资率
            material_cost: float, 材料总成本
        返回:
            total_cost: float, 总生产成本
            cost_breakdown: dict, 成本构成
        """
        labor_cost = labor_hours * hourly_rate
        total_cost = labor_cost + material_cost
        cost_breakdown = {
            'labor': labor_cost,
            'material': material_cost,
            'labor_percentage': labor_cost/total_cost,
            'material_percentage': material_cost/total_cost
        }
        return total_cost, cost_breakdown

class AutomotiveSupplyChainAnalysis:
    """汽车供应链分析类"""
    
    def __init__(self):
        """初始化供应链分析工具"""
        pass

    def supplier_evaluation(self, suppliers):
        """供应商评估
        参数:
            suppliers: list of dict, 供应商信息列表
        返回:
            ranked_suppliers: list, 按评分排序的供应商
        """
        # 评估标准: 价格(30%)、质量(40%)、交货时间(30%)
        for supplier in suppliers:
            price_score = (1 - supplier['price']/max(s['price'] for s in suppliers)) * 0.3
            quality_score = supplier['quality'] * 0.4
            delivery_score = (1 - supplier['delivery_time']/max(s['delivery_time'] for s in suppliers)) * 0.3
            supplier['score'] = price_score + quality_score + delivery_score
        
        return sorted(suppliers, key=lambda x: x['score'], reverse=True)

    def logistics_cost_analysis(self, distance, weight, rate_per_km):
        """物流成本分析
        参数:
            distance: float, 运输距离(km)
            weight: float, 货物重量(kg)
            rate_per_km: float, 每公里费率
        返回:
            logistics_cost: float, 物流总成本
        """
        return distance * weight * rate_per_km

class AIDataAnalysisAgent:
    """智能数据分析代理"""
    
    def __init__(self):
        """初始化智能分析引擎"""
        self.analysis_history = []
    
    def auto_analyze(self, data):
        """
        自动化数据分析
        参数:
            data: 输入数据(DataFrame或数组)
        返回:
            分析结果报告(dict)
        """
        # 自动检测数据类型并选择合适的分析方法
        if isinstance(data, pd.DataFrame):
            report = {
                'summary_stats': data.describe().to_dict(),
                'correlation_matrix': data.corr().to_dict(),
                'missing_values': data.isnull().sum().to_dict()
            }
        else:
            report = {
                'mean': np.mean(data),
                'std': np.std(data),
                'min': np.min(data),
                'max': np.max(data)
            }
        
        self.analysis_history.append(report)
        return report

class PredictionModelAgent:
    """预测模型代理"""
    
    def __init__(self):
        """初始化预测模型"""
        self.models = {}
    
    def train_predictive_model(self, X, y, model_type='random_forest'):
        """
        训练预测模型
        参数:
            X: 特征数据
            y: 目标变量
            model_type: 模型类型(默认随机森林)
        返回:
            训练好的模型
        """
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
        
        if model_type == 'random_forest':
            model = RandomForestRegressor()
            model.fit(X_train, y_train)
            self.models[model_type] = model
            return model
        
        raise ValueError(f"未知模型类型: {model_type}")

class OptimizationAgent:
    """优化决策代理"""
    
    def optimize_parameters(self, objective_func, bounds, max_iter=100):
        """
        参数优化
        参数:
            objective_func: 目标函数
            bounds: 参数边界
            max_iter: 最大迭代次数
        返回:
            最优参数和结果
        """
        from scipy.optimize import differential_evolution
        result = differential_evolution(objective_func, bounds, maxiter=max_iter)
        return {
            'optimal_params': result.x,
            'optimal_value': result.fun,
            'success': result.success
        }

class UserInteractionAgent:
    """用户交互代理"""
    
    def __init__(self):
        self.context = {}
    
    def natural_language_query(self, query):
        """
        自然语言查询处理
        参数:
            query: 用户自然语言查询
        返回:
            结构化查询结果
        """
        # 简化的自然语言处理
        if "预测" in query or "forecast" in query.lower():
            return {'intent': 'prediction', 'params': {}}
        elif "分析" in query or "analyze" in query.lower():
            return {'intent': 'analysis', 'params': {}}
        else:
            return {'intent': 'unknown', 'params': {}}

def main():
    """主程序入口"""
    print("汽车行业与数学研究综合程序(智能增强版)")
    print("1. 汽车市场数据分析")
    print("2. 汽车技术参数计算") 
    print("3. 数学建模应用")
    print("4. 汽车成本分析")
    print("5. 汽车供应链分析")
    print("6. 智能体技术支持")
    
    # 初始化所有功能模块
    market = AutomotiveMarketAnalysis()
    tech = AutomotiveTechnicalCalculations()
    math = MathematicalResearch()
    cost = AutomotiveCostAnalysis()
    supply_chain = AutomotiveSupplyChainAnalysis()
    
    # 初始化智能体
    ai_agent = AIDataAnalysisAgent()
    pred_agent = PredictionModelAgent()
    opt_agent = OptimizationAgent()
    ui_agent = UserInteractionAgent()

    while True:
        choice = input("请输入选项(1-6, q退出): ")
        
        if choice == '1':
            # 市场数据分析逻辑...
            pass
        elif choice == '6':
            print("\n智能体技术演示:")
            # 示例1: 智能数据分析
            sample_data = pd.DataFrame({
                'price': [20, 25, 30, 35, 40],
                'sales': [100, 120, 150, 130, 160]
            })
            print("\n智能数据分析示例:")
            print(ai_agent.auto_analyze(sample_data))
            
            # 示例2: 预测模型
            X = np.array([[1], [2], [3], [4], [5]])
            y = np.array([2, 4, 6, 8, 10])
            print("\n预测模型训练示例:")
            model = pred_agent.train_predictive_model(X, y)
            print(f"模型训练完成，特征重要性: {model.feature_importances_}")
            
            # 示例3: 自然语言交互
            print("\n自然语言交互示例:")
            query = "请分析最近的销售数据"
            print(f"查询: '{query}'")
            print("解析结果:", ui_agent.natural_language_query(query))
            
        elif choice.lower() == 'q':
            break
    
    # 这里可以添加更多交互逻辑

if __name__ == "__main__":
    main()